30 citations · 81 across the 15 of their papers we have counts for
18 papers · 1 filter
Generative Model Proposal based Particle Filtering for Data Assimilation
Chandni Nagda, Mayank Shrivastava, Gudrun Thorkelsdottir +3
Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the pos…
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu +1
We consider the problem of optimization of deep learning models with smooth activation functions. While there exist influential results on the problem from the ``near initializatio…
Noisy Truncated SGD: Optimization and Generalization
Yingxue Zhou, Xinyan Li, Arindam Banerjee
Recent empirical work on stochastic gradient descent (SGD) applied to over-parameterized deep learning has shown that most gradient components over epochs are quite small. Inspired…
Experiments with Rich Regime Training for Deep Learning
Xinyan Li, Arindam Banerjee
In spite of advances in understanding lazy training, recent work attributes the practical success of deep learning to the rich regime with complex inductive bias. In this paper, we…
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification
Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee
Differentially private SGD (DP-SGD) is one of the most popular methods for solving differentially private empirical risk minimization (ERM). Due to its noisy perturbation on each g…
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
Sijie He, Xinyan Li, Timothy DelSole +2
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would ha…